#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
VideoMAE模型实现
基于掩码自编码器的视频Transformer
参考：https://github.com/MCG-NJU/VideoMAE
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class PatchEmbed(nn.Module):
    """视频帧分块嵌入"""
    
    def __init__(self, img_size=224, patch_size=16, in_channels=3, embed_dim=768, 
                 tubelet_size=2, norm_layer=None):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.tubelet_size = tubelet_size
        
        self.num_patches = ((img_size // patch_size) ** 2) * (tubelet_size)
        self.proj = nn.Conv3d(
            in_channels=in_channels,
            out_channels=embed_dim,
            kernel_size=(tubelet_size, patch_size, patch_size),
            stride=(tubelet_size, patch_size, patch_size)
        )
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
    
    def forward(self, x):
        B, C, T, H, W = x.shape
        x = self.proj(x)
        x = x.flatten(2).transpose(1, 2)  # B, N, C
        x = self.norm(x)
        return x


class Attention(nn.Module):
    """多头自注意力机制"""
    
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
    
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class MLP(nn.Module):
    """前馈神经网络"""
    
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
    
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(nn.Module):
    """Transformer编码器块"""
    
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        
        # 对于简单实现，我们省略了drop_path
        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.drop_path = nn.Identity()
        
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
    
    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class VideoMAE(nn.Module):
    """VideoMAE模型"""
    
    def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=7,
                 embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 num_frames=16, tubelet_size=2, pretrained=False, feature_dim=768, dropout=0.5):
        super().__init__()
        self.num_classes = num_classes
        self.num_features = embed_dim  # 特征维度
        self.embed_dim = embed_dim
        
        # 分块嵌入
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_channels=in_channels,
            embed_dim=embed_dim, tubelet_size=tubelet_size, norm_layer=norm_layer
        )
        num_patches = self.patch_embed.num_patches
        
        # 位置嵌入
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)
        
        # 随机深度
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # 随深度线性递增的随机深度比率
        
        # Transformer编码器
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], 
                norm_layer=norm_layer
            )
            for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)
        
        # 分类头
        self.fc_norm = norm_layer(embed_dim)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else nn.Identity()
        self.fc = nn.Linear(embed_dim, feature_dim)
        self.head = nn.Linear(feature_dim, num_classes)
        
        # 初始化
        self._init_weights()
        
        # 加载预训练权重
        if pretrained:
            self._load_pretrained_weights()
    
    def _init_weights(self):
        """初始化权重"""
        # 初始化位置嵌入
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        
        # 初始化其他参数
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.LayerNorm):
                nn.init.zeros_(m.bias)
                nn.init.ones_(m.weight)
    
    def _load_pretrained_weights(self):
        """加载预训练权重"""
        # 实际中，这里应该加载预训练的VideoMAE权重
        # 出于示例目的，我们在这里只是打印一条消息
        print("加载预训练权重的代码将在实际使用时实现")
    
    def forward_features(self, x):
        """提取特征"""
        # 输入应为 [B, C, T, H, W]
        B = x.shape[0]
        x = self.patch_embed(x)  # [B, num_patches, embed_dim]
        
        # 添加位置嵌入
        x = x + self.pos_embed
        x = self.pos_drop(x)
        
        # 通过Transformer块
        for blk in self.blocks:
            x = blk(x)
        
        x = self.norm(x)
        
        # 全局池化，取序列的平均值
        x = self.fc_norm(x.mean(dim=1))
        
        return x
    
    def forward(self, x):
        """前向传播"""
        x = self.forward_features(x)
        x = self.dropout(x)
        x = self.fc(x)
        x = self.head(x)
        return x 